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Strong Method Problem Solving

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The search graph as described by the contents of WM data-driven BFS

ES examples - DENDRAL(Russell & Norvig, 2003)

Problem solved: inferring molecular structure from the information provided by a mass spectrometer. This is an important problem because the chemical and physical properties of compounds are determined not just by their constituent atoms, but by the arrangement of these atoms as well.

ES examples - DENDRAL(Russell & Norvig, 2003)

Inputs: elementary formula of the molecule (e.g., C6H13NO2), and the mass spectrum giving the masses of the various fragments of the molecule generated when it is bombarded by an electron beam (e.g., the mass spectrum might contain a peak at m=15, corresponding to the mass of a methyl (CH3) fragment.

ES examples - DENDRAL (cont’d)

Naïve version: DENDRAL stands for DENDritic Algoritm: a procedure to exhaustively and nonredundantly enumerate all the topologically distinct arrangements of any given set of atoms. Generate all the possible structures consistent with the formula, predict what mass spectrum would be observed for each, compare this with the actual spectrum.This is intractable for large molecules!

Improved version: look for well-known patterns of peaks in the spectrum that suggested common substructures in the molecule. This reduces the number of possible candidates enormously.

ES examples - DENDRAL (cont’d)

A rule to recognize a ketone (C=0) subgroup (weighs 28)

if there are two peaks at x1 and x2 such that(a) x1 + x2 = M + 28 (M is the mass of the whole molecule);(b) x1 - 28 is a high peak(c) x2 - 28 is a high peak(d) at least one of x1 and x2 is highthen there is a ketone subgroup

Cyclopropyl-methyl-ketone

Dicyclopropyl-methyl-ketone

ES examples - MYCIN

Problem solved: diagnose blood infections. This is an important problem because physicians usually must begin antibiotic treatment without knowing what the organism is (laboratory cultures take time). They have two choices: (1) prescribe a broad spectrum drug (2) prescribe a disease-specific drug (better)

.

ES examples - MYCIN (cont’d)

Differences from DENDRAL:

No general theoretical model existed from which MYCIN rules could be deduced. They had to be acquired from extensive interviewing of experts, who in turn acquired them from textbooks, other experts, and direct experience of cases.

ES examples - MYCIN (cont’d)

About 450 rules. One example is:

If the site of the culture is blood the gram of the organism is neg the morphology of the organism is rod the burn of the patient is seriousthen there is weakly suggestive evidence (0.4) that the identity of the organism is pseudomonas.

ES examples - MYCIN (cont’d)

If the infection which requires therapy is meningitis only circumstantial evidence is available for this case the type of the infection is bacterial the patient is receiving corticosteroids then there is evidence that the organisms which might be causing the infection are e.coli(0.4), klebsiella- pneumonia(0.2), or pseudomonas-aeruginosa(0.1).

ES examples - MYCIN (cont’d)

Starting rule: “If there is an organism requiring therapy, then, compute the possible therapies and pick the best one.”

It first tries to see if the disease is known. Otherwise, tries to find it out.

ES examples - MYCIN (cont’d)

Can ask questions during the process:

> What is the patient’s name?John Doe.> Male or female?Male.>Age?He is 55.> Have you obtained positive cultures indicating general type?Yes.> What type of infection is it?Primary bacteremia.

ES examples - MYCIN (cont’d)

> Let’s call the first significant organismfrom this culture U1. Do you know theidentity of U1?No.> Is U1 a rod or a coccus or something else?Rod.> What is the gram stain of U1?Gram-negative.

In the last two questions, it is trying to ask the most general question possible, so that repeated questions of the same type do not annoy the user. The format of the KB should make the questions reasonable.

ES examples - MYCIN (cont’d)

Studies about its performance showed its recommendations were as well as some experts, and considerably better than junior doctors.

Could calculate drug dosages very precisely.

Dealt well with drug interactions.

Had good explanation features and rule acquisition systems.

Was narrow in scope (not a large set of diseases). Another expert system, INTERNIST, knows about internal medicine.

Issues in doctors’ egos, legal aspects.

Asking questions to the user

Which questions should be asked and in what order?

Try to ask questions to make facilitate a more comfortable dialogue. For instance, ask related questions rather than bouncing between unrelated topics (e.g., zipcode as part of an address or to relate the evidence to the area the patient lives).

ES examples - R1 (or XCON)

The first commercial expert system (~1982).

Developed at Digital Equipment Corporation (DEC).

Problem solved: Configure orders for new computer systems. Each customer order was generally a variety of computer products not guaranteed to be compatible with one another (conversion cards, cabling, support software…)

By 1986, it was saving the company $40 million a year. Previously, each customer shipment had to be tested for compatibility as an assembly before being shipper. By 1988, DEC’s AI group had 40 expert systems deployed.

ES examples - R1 (or XCON) (cont’d)

Rules to match computers and their peripherals:

“If the Stockman 800 printer and DPK202 computer have been selected, add a printer conversion card, because they are not compatible.”

Being able to change the rule base easily was an important issue because the products were always changing.

Over 99% of the configurations were reported to be accurate. Errors were due to lack of product information on recent products (easily correctible.) Like MYCIN, performs as well as or better than most experts.

6,000 - 10,000 rules.

Expert Systems: then and now

The AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988.

Nearly every major U.S. corporation had its own AI group and was either using or investigating expert systems.

For instance, Du Pont had 100 ESs in use and 500 in development, saving an estimated $10 million per year.

AAAI had 15,000 members during the “expert systems craze.”

Soon a period called the “AI Winter” came…BIRRR...

Expert Systems: then and now (cont’d)

The AI industry has shifted focus and stabilized (AAAI members 5500- 7000)

Expert systems continue to save companies money

IBM’s San Jose facility has an ES that diagnoses problems on disk drives

Pac Bell’s diagnoses computer network problems

Boeing’s tells workers how to assemble electrical connectors

American Express Co’s helps in card application approvals

Met Life’s processes mortgage applications

Expert Sytem Shells: abstract away the details to produce an inference engine that might be useful for other tasks. Many are available.

Heuristics and control in expert systems

organization of a rule’s premises

rule order

costs of different tests

which rules to select:

refraction

recency

specificity

restrict potentially usable rules

Model-based reasoning

Attempt to describe the “inner details” of the system.

This way, the expert system (or any other knowledge-intensive program) can revert to first principles, and can still make inferences if rules summarizing the situation are not present.

For our logic system to work, we’ll have to define such an axiom for each action and for each predicate.

This is called the frame problem.

Perhaps the time to get “un-logical”.

The STRIPS representation

No frame problem.

Special purpose representation.

An operator is defined in terms of its:

name,parameters,preconditions, andresults.

A planner is a special purpose algorithm rather than a general purpose logic theorem prover:forward or backward chaining (state space),plan space algorithms, and several significant others including logic-based.

Four operators for the blocks world

P: gripping()  clear(X)  ontable(X)

pickup(X)A: gripping(X)

D: ontable(X)  gripping()

P: gripping(X)

putdown(X)A: ontable(X)  gripping()  clear(X)

D: gripping(X)

P: gripping(X)  clear(Y)

stack(X,Y)A: on(X,Y)  gripping()  clear(X)

D: gripping(X)  clear(Y)

P: gripping()  clear(X)  on(X,Y)

unstack(X,Y)A: gripping(X)  clear(Y)

D: on(X,Y)  gripping()

Notice the simplification

Preconditions, add lists, and delete lists are all conjunctions. We no more have the full power of predicate logic.

The same applies to goals. Goals are conjunctions of predicates.

A detail:

Why do we have two operators for picking up (pickup and unstack), and two for putting down (putdown and stack)?

A goal state for the blocks world

A state space algorithm for STRIPS operators

Search the space of situations (or states). This means each node in the search tree is a state.

The root of the tree is the start state.

Operators are the means of transition from each node to its children.

The goal test involves seeing if the set of goals is a subset of the current situation.

Why is the frame problem no more a problem?

Now, the following graph makes much more sense

Problems in representation

Frame problem: List everything that does not change. It no more is a significant problem because what is not listed as changing (via the add and delete lists) is assumed to be not changing.

Qualification problem: Can we list every precondition for an action? For instance, in order for PICKUP to work, the block should not be glued to the table, it should not be nailed to the table, …

It still is a problem. A partial solution is to prioritize preconditions, i.e., separate out the preconditions that are worth achieving.

Problems in representation (cont’d)

Ramification problem: Can we list every result of an action? For instance, if a block is picked up its shadow changes location, the weight on the table decreases, ...

It still is a problem. A partial solution is to code rules so that inferences can be made. For instance, allow rules to calculate where the shadow would be, given the positions of the light source and the object. When the position of the object changes, its shadow changes too.

Why is planning a hard problem?

It is due to the large branching factor and the overwhelming number of possibilities.

There is usually no way to separate out the relevant operators. Take the previous example, and imagine that there are 100 balls, just two rooms, and two grippers. Again, the goal is to take 1 ball to the other room.

How many PICK operators are possible in the initial situation?

pick:parameters (?obj ?room ?gripper)

That is only one part of the branching factor, the robot could also move without picking up anything.

Why is planning a hard problem? (cont’d)

Also, goal interactions is a major problem. In planning, goal-directed search seems to make much more sense, but unfortunately cannot address the exponential explosion. This time, the branching factor increases due to the many ways of resolving interactions.

When subgoals are compatible, i.e., they do not interact, they are said to be linear ( or independent, or serializable).

How to deal with the exponential explosion?

Use goal-directed algorithms

Use domain-independent heuristics

Use domain-dependent heuristics (need a language to specify them)

The “monkey and bananas” problem

The “monkey and bananas” problem(cont’d)

The problem statement: A monkey is in a laboratory room containing a box, a knife and a bunch of bananas. The bananas are hanging from the ceiling out of the reach of the monkey. How can the monkey obtain the bananas?